Overview

Dataset statistics

Number of variables20
Number of observations5630
Missing cells1856
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory879.8 KiB
Average record size in memory160.0 B

Variable types

Numeric11
Categorical9

Alerts

CustomerID is highly overall correlated with HourSpendOnApp and 3 other fieldsHigh correlation
CityTier is highly overall correlated with PreferredPaymentModeHigh correlation
PreferredPaymentMode is highly overall correlated with CityTierHigh correlation
HourSpendOnApp is highly overall correlated with CustomerID and 1 other fieldsHigh correlation
NumberOfDeviceRegistered is highly overall correlated with CustomerID and 1 other fieldsHigh correlation
PreferredOrderCat is highly overall correlated with CashbackAmountHigh correlation
SatisfactionScore is highly overall correlated with CustomerIDHigh correlation
MaritalStatus is highly overall correlated with CustomerIDHigh correlation
CouponUsed is highly overall correlated with OrderCountHigh correlation
OrderCount is highly overall correlated with CouponUsedHigh correlation
CashbackAmount is highly overall correlated with PreferredOrderCatHigh correlation
Tenure has 264 (4.7%) missing valuesMissing
WarehouseToHome has 251 (4.5%) missing valuesMissing
HourSpendOnApp has 255 (4.5%) missing valuesMissing
OrderAmountHikeFromlastYear has 265 (4.7%) missing valuesMissing
CouponUsed has 256 (4.5%) missing valuesMissing
OrderCount has 258 (4.6%) missing valuesMissing
DaySinceLastOrder has 307 (5.5%) missing valuesMissing
CustomerID is uniformly distributedUniform
CustomerID has unique valuesUnique
Tenure has 508 (9.0%) zerosZeros
CouponUsed has 1030 (18.3%) zerosZeros
DaySinceLastOrder has 496 (8.8%) zerosZeros

Reproduction

Analysis started2024-07-09 02:24:41.847073
Analysis finished2024-07-09 02:25:28.952761
Duration47.11 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

CustomerID
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct5630
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52815.5
Minimum50001
Maximum55630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2024-07-09T07:55:29.174091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum50001
5-th percentile50282.45
Q151408.25
median52815.5
Q354222.75
95-th percentile55348.55
Maximum55630
Range5629
Interquartile range (IQR)2814.5

Descriptive statistics

Standard deviation1625.3853
Coefficient of variation (CV)0.030774779
Kurtosis-1.2
Mean52815.5
Median Absolute Deviation (MAD)1407.5
Skewness0
Sum2.9735126 × 108
Variance2641877.5
MonotonicityStrictly increasing
2024-07-09T07:55:29.598778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50001 1
 
< 0.1%
53751 1
 
< 0.1%
53759 1
 
< 0.1%
53758 1
 
< 0.1%
53757 1
 
< 0.1%
53756 1
 
< 0.1%
53755 1
 
< 0.1%
53754 1
 
< 0.1%
53753 1
 
< 0.1%
53752 1
 
< 0.1%
Other values (5620) 5620
99.8%
ValueCountFrequency (%)
50001 1
< 0.1%
50002 1
< 0.1%
50003 1
< 0.1%
50004 1
< 0.1%
50005 1
< 0.1%
50006 1
< 0.1%
50007 1
< 0.1%
50008 1
< 0.1%
50009 1
< 0.1%
50010 1
< 0.1%
ValueCountFrequency (%)
55630 1
< 0.1%
55629 1
< 0.1%
55628 1
< 0.1%
55627 1
< 0.1%
55626 1
< 0.1%
55625 1
< 0.1%
55624 1
< 0.1%
55623 1
< 0.1%
55622 1
< 0.1%
55621 1
< 0.1%

Churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
0
4682 
1
948 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Length

2024-07-09T07:55:29.969316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T07:55:30.223845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Most occurring characters

ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Tenure
Real number (ℝ)

MISSING  ZEROS 

Distinct36
Distinct (%)0.7%
Missing264
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean10.189899
Minimum0
Maximum61
Zeros508
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2024-07-09T07:55:30.554055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q316
95-th percentile27
Maximum61
Range61
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.557241
Coefficient of variation (CV)0.83977679
Kurtosis-0.0073694695
Mean10.189899
Median Absolute Deviation (MAD)7
Skewness0.73651338
Sum54679
Variance73.226373
MonotonicityNot monotonic
2024-07-09T07:55:30.941050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 690
 
12.3%
0 508
 
9.0%
8 263
 
4.7%
9 247
 
4.4%
7 221
 
3.9%
10 213
 
3.8%
5 204
 
3.6%
4 203
 
3.6%
3 195
 
3.5%
11 194
 
3.4%
Other values (26) 2428
43.1%
(Missing) 264
 
4.7%
ValueCountFrequency (%)
0 508
9.0%
1 690
12.3%
2 167
 
3.0%
3 195
 
3.5%
4 203
 
3.6%
5 204
 
3.6%
6 183
 
3.3%
7 221
 
3.9%
8 263
 
4.7%
9 247
 
4.4%
ValueCountFrequency (%)
61 1
 
< 0.1%
60 1
 
< 0.1%
51 1
 
< 0.1%
50 1
 
< 0.1%
31 49
0.9%
30 66
1.2%
29 55
1.0%
28 70
1.2%
27 66
1.2%
26 60
1.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Phone
3996 
Computer
1634 

Length

Max length8
Median length5
Mean length5.8706927
Min length5

Characters and Unicode

Total characters33052
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhone
2nd rowPhone
3rd rowPhone
4th rowPhone
5th rowPhone

Common Values

ValueCountFrequency (%)
Phone 3996
71.0%
Computer 1634
29.0%

Length

2024-07-09T07:55:31.322708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T07:55:31.621032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
phone 3996
71.0%
computer 1634
29.0%

Most occurring characters

ValueCountFrequency (%)
o 5630
17.0%
e 5630
17.0%
P 3996
12.1%
h 3996
12.1%
n 3996
12.1%
C 1634
 
4.9%
m 1634
 
4.9%
p 1634
 
4.9%
u 1634
 
4.9%
t 1634
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5630
17.0%
e 5630
17.0%
P 3996
12.1%
h 3996
12.1%
n 3996
12.1%
C 1634
 
4.9%
m 1634
 
4.9%
p 1634
 
4.9%
u 1634
 
4.9%
t 1634
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5630
17.0%
e 5630
17.0%
P 3996
12.1%
h 3996
12.1%
n 3996
12.1%
C 1634
 
4.9%
m 1634
 
4.9%
p 1634
 
4.9%
u 1634
 
4.9%
t 1634
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5630
17.0%
e 5630
17.0%
P 3996
12.1%
h 3996
12.1%
n 3996
12.1%
C 1634
 
4.9%
m 1634
 
4.9%
p 1634
 
4.9%
u 1634
 
4.9%
t 1634
 
4.9%

CityTier
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
1
3666 
3
1722 
2
 
242

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

Length

2024-07-09T07:55:31.912838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T07:55:32.193737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

Most occurring characters

ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

WarehouseToHome
Real number (ℝ)

MISSING 

Distinct34
Distinct (%)0.6%
Missing251
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean15.639896
Minimum5
Maximum127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2024-07-09T07:55:32.507648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median14
Q320
95-th percentile33
Maximum127
Range122
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.5314752
Coefficient of variation (CV)0.54549437
Kurtosis9.9869304
Mean15.639896
Median Absolute Deviation (MAD)5
Skewness1.6191537
Sum84127
Variance72.786069
MonotonicityNot monotonic
2024-07-09T07:55:32.922174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
9 559
 
9.9%
8 444
 
7.9%
7 389
 
6.9%
16 322
 
5.7%
14 299
 
5.3%
6 295
 
5.2%
15 288
 
5.1%
10 274
 
4.9%
13 249
 
4.4%
11 233
 
4.1%
Other values (24) 2027
36.0%
(Missing) 251
 
4.5%
ValueCountFrequency (%)
5 8
 
0.1%
6 295
5.2%
7 389
6.9%
8 444
7.9%
9 559
9.9%
10 274
4.9%
11 233
4.1%
12 221
 
3.9%
13 249
4.4%
14 299
5.3%
ValueCountFrequency (%)
127 1
 
< 0.1%
126 1
 
< 0.1%
36 51
0.9%
35 93
1.7%
34 63
1.1%
33 67
1.2%
32 94
1.7%
31 101
1.8%
30 94
1.7%
29 81
1.4%

PreferredPaymentMode
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Debit Card
2314 
Credit Card
1774 
E wallet
614 
Cash on Delivery
514 
UPI
414 

Length

Max length16
Median length11
Mean length10.130018
Min length3

Characters and Unicode

Total characters57032
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDebit Card
2nd rowUPI
3rd rowDebit Card
4th rowDebit Card
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Debit Card 2314
41.1%
Credit Card 1774
31.5%
E wallet 614
 
10.9%
Cash on Delivery 514
 
9.1%
UPI 414
 
7.4%

Length

2024-07-09T07:55:33.496083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T07:55:33.767370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
card 4088
36.0%
debit 2314
20.4%
credit 1774
15.6%
e 614
 
5.4%
wallet 614
 
5.4%
cash 514
 
4.5%
on 514
 
4.5%
delivery 514
 
4.5%
upi 414
 
3.6%

Most occurring characters

ValueCountFrequency (%)
C 6376
11.2%
r 6376
11.2%
d 5862
10.3%
e 5730
10.0%
5730
10.0%
a 5216
9.1%
t 4702
8.2%
i 4602
8.1%
D 2828
 
5.0%
b 2314
 
4.1%
Other values (12) 7296
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 6376
11.2%
r 6376
11.2%
d 5862
10.3%
e 5730
10.0%
5730
10.0%
a 5216
9.1%
t 4702
8.2%
i 4602
8.1%
D 2828
 
5.0%
b 2314
 
4.1%
Other values (12) 7296
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 6376
11.2%
r 6376
11.2%
d 5862
10.3%
e 5730
10.0%
5730
10.0%
a 5216
9.1%
t 4702
8.2%
i 4602
8.1%
D 2828
 
5.0%
b 2314
 
4.1%
Other values (12) 7296
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 6376
11.2%
r 6376
11.2%
d 5862
10.3%
e 5730
10.0%
5730
10.0%
a 5216
9.1%
t 4702
8.2%
i 4602
8.1%
D 2828
 
5.0%
b 2314
 
4.1%
Other values (12) 7296
12.8%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Male
3384 
Female
2246 

Length

Max length6
Median length4
Mean length4.7978686
Min length4

Characters and Unicode

Total characters27012
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 3384
60.1%
Female 2246
39.9%

Length

2024-07-09T07:55:34.147946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T07:55:34.503734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
male 3384
60.1%
female 2246
39.9%

Most occurring characters

ValueCountFrequency (%)
e 7876
29.2%
a 5630
20.8%
l 5630
20.8%
M 3384
12.5%
F 2246
 
8.3%
m 2246
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7876
29.2%
a 5630
20.8%
l 5630
20.8%
M 3384
12.5%
F 2246
 
8.3%
m 2246
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7876
29.2%
a 5630
20.8%
l 5630
20.8%
M 3384
12.5%
F 2246
 
8.3%
m 2246
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7876
29.2%
a 5630
20.8%
l 5630
20.8%
M 3384
12.5%
F 2246
 
8.3%
m 2246
 
8.3%

HourSpendOnApp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.1%
Missing255
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2.9315349
Minimum0
Maximum5
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2024-07-09T07:55:34.796024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72192585
Coefficient of variation (CV)0.24626207
Kurtosis-0.66707614
Mean2.9315349
Median Absolute Deviation (MAD)1
Skewness-0.027212622
Sum15757
Variance0.52117693
MonotonicityNot monotonic
2024-07-09T07:55:35.071866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2687
47.7%
2 1471
26.1%
4 1176
20.9%
1 35
 
0.6%
0 3
 
0.1%
5 3
 
0.1%
(Missing) 255
 
4.5%
ValueCountFrequency (%)
0 3
 
0.1%
1 35
 
0.6%
2 1471
26.1%
3 2687
47.7%
4 1176
20.9%
5 3
 
0.1%
ValueCountFrequency (%)
5 3
 
0.1%
4 1176
20.9%
3 2687
47.7%
2 1471
26.1%
1 35
 
0.6%
0 3
 
0.1%

NumberOfDeviceRegistered
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6889876
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2024-07-09T07:55:35.370133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0239985
Coefficient of variation (CV)0.27758253
Kurtosis0.58284873
Mean3.6889876
Median Absolute Deviation (MAD)1
Skewness-0.39696864
Sum20769
Variance1.048573
MonotonicityNot monotonic
2024-07-09T07:55:35.700960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 2377
42.2%
3 1699
30.2%
5 881
 
15.6%
2 276
 
4.9%
1 235
 
4.2%
6 162
 
2.9%
ValueCountFrequency (%)
1 235
 
4.2%
2 276
 
4.9%
3 1699
30.2%
4 2377
42.2%
5 881
 
15.6%
6 162
 
2.9%
ValueCountFrequency (%)
6 162
 
2.9%
5 881
 
15.6%
4 2377
42.2%
3 1699
30.2%
2 276
 
4.9%
1 235
 
4.2%

PreferredOrderCat
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Phone
2080 
Laptop & Accessory
2050 
Fashion
826 
Grocery
410 
Others
264 

Length

Max length18
Median length7
Mean length10.219538
Min length5

Characters and Unicode

Total characters57536
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaptop & Accessory
2nd rowPhone
3rd rowPhone
4th rowLaptop & Accessory
5th rowPhone

Common Values

ValueCountFrequency (%)
Phone 2080
36.9%
Laptop & Accessory 2050
36.4%
Fashion 826
 
14.7%
Grocery 410
 
7.3%
Others 264
 
4.7%

Length

2024-07-09T07:55:36.071568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T07:55:36.417165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
phone 2080
21.4%
laptop 2050
21.1%
2050
21.1%
accessory 2050
21.1%
fashion 826
 
8.5%
grocery 410
 
4.2%
others 264
 
2.7%

Most occurring characters

ValueCountFrequency (%)
o 7416
12.9%
s 5190
 
9.0%
e 4804
 
8.3%
c 4510
 
7.8%
p 4100
 
7.1%
4100
 
7.1%
h 3170
 
5.5%
r 3134
 
5.4%
n 2906
 
5.1%
a 2876
 
5.0%
Other values (10) 15330
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 7416
12.9%
s 5190
 
9.0%
e 4804
 
8.3%
c 4510
 
7.8%
p 4100
 
7.1%
4100
 
7.1%
h 3170
 
5.5%
r 3134
 
5.4%
n 2906
 
5.1%
a 2876
 
5.0%
Other values (10) 15330
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 7416
12.9%
s 5190
 
9.0%
e 4804
 
8.3%
c 4510
 
7.8%
p 4100
 
7.1%
4100
 
7.1%
h 3170
 
5.5%
r 3134
 
5.4%
n 2906
 
5.1%
a 2876
 
5.0%
Other values (10) 15330
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 7416
12.9%
s 5190
 
9.0%
e 4804
 
8.3%
c 4510
 
7.8%
p 4100
 
7.1%
4100
 
7.1%
h 3170
 
5.5%
r 3134
 
5.4%
n 2906
 
5.1%
a 2876
 
5.0%
Other values (10) 15330
26.6%

SatisfactionScore
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
3
1698 
1
1164 
5
1108 
4
1074 
2
586 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

Length

2024-07-09T07:55:36.750086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T07:55:37.057784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

Most occurring characters

ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

MaritalStatus
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Married
2986 
Single
1796 
Divorced
848 

Length

Max length8
Median length7
Mean length6.8316163
Min length6

Characters and Unicode

Total characters38462
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 2986
53.0%
Single 1796
31.9%
Divorced 848
 
15.1%

Length

2024-07-09T07:55:37.483835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T07:55:37.848469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
married 2986
53.0%
single 1796
31.9%
divorced 848
 
15.1%

Most occurring characters

ValueCountFrequency (%)
r 6820
17.7%
i 5630
14.6%
e 5630
14.6%
d 3834
10.0%
M 2986
7.8%
a 2986
7.8%
S 1796
 
4.7%
n 1796
 
4.7%
g 1796
 
4.7%
l 1796
 
4.7%
Other values (4) 3392
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 6820
17.7%
i 5630
14.6%
e 5630
14.6%
d 3834
10.0%
M 2986
7.8%
a 2986
7.8%
S 1796
 
4.7%
n 1796
 
4.7%
g 1796
 
4.7%
l 1796
 
4.7%
Other values (4) 3392
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 6820
17.7%
i 5630
14.6%
e 5630
14.6%
d 3834
10.0%
M 2986
7.8%
a 2986
7.8%
S 1796
 
4.7%
n 1796
 
4.7%
g 1796
 
4.7%
l 1796
 
4.7%
Other values (4) 3392
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 6820
17.7%
i 5630
14.6%
e 5630
14.6%
d 3834
10.0%
M 2986
7.8%
a 2986
7.8%
S 1796
 
4.7%
n 1796
 
4.7%
g 1796
 
4.7%
l 1796
 
4.7%
Other values (4) 3392
8.8%

NumberOfAddress
Real number (ℝ)

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.214032
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2024-07-09T07:55:38.156569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile10
Maximum22
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5835855
Coefficient of variation (CV)0.6130911
Kurtosis0.95922927
Mean4.214032
Median Absolute Deviation (MAD)1
Skewness1.0886394
Sum23725
Variance6.6749141
MonotonicityNot monotonic
2024-07-09T07:55:38.503120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 1369
24.3%
3 1278
22.7%
4 588
10.4%
5 571
10.1%
6 382
 
6.8%
1 371
 
6.6%
8 280
 
5.0%
7 256
 
4.5%
9 239
 
4.2%
10 194
 
3.4%
Other values (5) 102
 
1.8%
ValueCountFrequency (%)
1 371
 
6.6%
2 1369
24.3%
3 1278
22.7%
4 588
10.4%
5 571
10.1%
6 382
 
6.8%
7 256
 
4.5%
8 280
 
5.0%
9 239
 
4.2%
10 194
 
3.4%
ValueCountFrequency (%)
22 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
11 98
 
1.7%
10 194
3.4%
9 239
4.2%
8 280
5.0%
7 256
4.5%
6 382
6.8%

Complain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
0
4026 
1
1604 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%

Length

2024-07-09T07:55:38.836186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T07:55:39.134683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%

Most occurring characters

ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%

OrderAmountHikeFromlastYear
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)0.3%
Missing265
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean15.707922
Minimum11
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2024-07-09T07:55:39.442319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q113
median15
Q318
95-th percentile23
Maximum26
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6754855
Coefficient of variation (CV)0.23398929
Kurtosis-0.28038119
Mean15.707922
Median Absolute Deviation (MAD)3
Skewness0.79078536
Sum84273
Variance13.509193
MonotonicityNot monotonic
2024-07-09T07:55:39.784836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
14 750
13.3%
13 741
13.2%
12 728
12.9%
15 542
9.6%
11 391
6.9%
16 333
5.9%
18 321
5.7%
19 311
5.5%
17 297
 
5.3%
20 243
 
4.3%
Other values (6) 708
12.6%
(Missing) 265
 
4.7%
ValueCountFrequency (%)
11 391
6.9%
12 728
12.9%
13 741
13.2%
14 750
13.3%
15 542
9.6%
16 333
5.9%
17 297
 
5.3%
18 321
5.7%
19 311
5.5%
20 243
 
4.3%
ValueCountFrequency (%)
26 33
 
0.6%
25 73
 
1.3%
24 84
 
1.5%
23 144
2.6%
22 184
3.3%
21 190
3.4%
20 243
4.3%
19 311
5.5%
18 321
5.7%
17 297
5.3%

CouponUsed
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)0.3%
Missing256
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean1.7510234
Minimum0
Maximum16
Zeros1030
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2024-07-09T07:55:40.130160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile6
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8946214
Coefficient of variation (CV)1.082008
Kurtosis9.1322812
Mean1.7510234
Median Absolute Deviation (MAD)1
Skewness2.5456526
Sum9410
Variance3.5895904
MonotonicityNot monotonic
2024-07-09T07:55:40.460667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 2105
37.4%
2 1283
22.8%
0 1030
18.3%
3 327
 
5.8%
4 197
 
3.5%
5 129
 
2.3%
6 108
 
1.9%
7 89
 
1.6%
8 42
 
0.7%
10 14
 
0.2%
Other values (7) 50
 
0.9%
(Missing) 256
 
4.5%
ValueCountFrequency (%)
0 1030
18.3%
1 2105
37.4%
2 1283
22.8%
3 327
 
5.8%
4 197
 
3.5%
5 129
 
2.3%
6 108
 
1.9%
7 89
 
1.6%
8 42
 
0.7%
9 13
 
0.2%
ValueCountFrequency (%)
16 2
 
< 0.1%
15 1
 
< 0.1%
14 5
 
0.1%
13 8
 
0.1%
12 9
 
0.2%
11 12
 
0.2%
10 14
 
0.2%
9 13
 
0.2%
8 42
0.7%
7 89
1.6%

OrderCount
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)0.3%
Missing258
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean3.0080045
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2024-07-09T07:55:40.790468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9396795
Coefficient of variation (CV)0.97728563
Kurtosis4.7184661
Mean3.0080045
Median Absolute Deviation (MAD)1
Skewness2.1964141
Sum16159
Variance8.6417158
MonotonicityNot monotonic
2024-07-09T07:55:41.096944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 2025
36.0%
1 1751
31.1%
3 371
 
6.6%
7 206
 
3.7%
4 204
 
3.6%
5 181
 
3.2%
8 172
 
3.1%
6 137
 
2.4%
9 62
 
1.1%
12 54
 
1.0%
Other values (6) 209
 
3.7%
(Missing) 258
 
4.6%
ValueCountFrequency (%)
1 1751
31.1%
2 2025
36.0%
3 371
 
6.6%
4 204
 
3.6%
5 181
 
3.2%
6 137
 
2.4%
7 206
 
3.7%
8 172
 
3.1%
9 62
 
1.1%
10 36
 
0.6%
ValueCountFrequency (%)
16 23
 
0.4%
15 33
 
0.6%
14 36
 
0.6%
13 30
 
0.5%
12 54
 
1.0%
11 51
 
0.9%
10 36
 
0.6%
9 62
 
1.1%
8 172
3.1%
7 206
3.7%

DaySinceLastOrder
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)0.4%
Missing307
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean4.5434905
Minimum0
Maximum46
Zeros496
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2024-07-09T07:55:41.472937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum46
Range46
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6544332
Coefficient of variation (CV)0.80432284
Kurtosis4.0239643
Mean4.5434905
Median Absolute Deviation (MAD)2
Skewness1.1909995
Sum24185
Variance13.354882
MonotonicityNot monotonic
2024-07-09T07:55:41.808820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 900
16.0%
2 792
14.1%
1 614
10.9%
8 538
9.6%
0 496
8.8%
7 447
7.9%
4 431
7.7%
9 299
 
5.3%
5 228
 
4.0%
10 157
 
2.8%
Other values (12) 421
7.5%
(Missing) 307
 
5.5%
ValueCountFrequency (%)
0 496
8.8%
1 614
10.9%
2 792
14.1%
3 900
16.0%
4 431
7.7%
5 228
 
4.0%
6 113
 
2.0%
7 447
7.9%
8 538
9.6%
9 299
 
5.3%
ValueCountFrequency (%)
46 1
 
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
18 10
 
0.2%
17 17
 
0.3%
16 13
 
0.2%
15 19
 
0.3%
14 35
0.6%
13 51
0.9%
12 69
1.2%

CashbackAmount
Real number (ℝ)

HIGH CORRELATION 

Distinct2586
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.22303
Minimum0
Maximum324.99
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2024-07-09T07:55:42.199833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile123.0335
Q1145.77
median163.28
Q3196.3925
95-th percentile291.9385
Maximum324.99
Range324.99
Interquartile range (IQR)50.6225

Descriptive statistics

Standard deviation49.207036
Coefficient of variation (CV)0.27765599
Kurtosis0.97450522
Mean177.22303
Median Absolute Deviation (MAD)23.035
Skewness1.1498457
Sum997765.66
Variance2421.3324
MonotonicityNot monotonic
2024-07-09T07:55:42.616707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123.42 8
 
0.1%
149.36 8
 
0.1%
148.42 8
 
0.1%
188.47 7
 
0.1%
154.73 7
 
0.1%
180.62 6
 
0.1%
126.1 6
 
0.1%
125.59 6
 
0.1%
153.04 6
 
0.1%
146.27 6
 
0.1%
Other values (2576) 5562
98.8%
ValueCountFrequency (%)
0 4
0.1%
12 1
 
< 0.1%
25 4
0.1%
37 1
 
< 0.1%
56 1
 
< 0.1%
81 1
 
< 0.1%
110.09 2
< 0.1%
110.51 2
< 0.1%
110.52 2
< 0.1%
110.81 2
< 0.1%
ValueCountFrequency (%)
324.99 2
< 0.1%
324.73 2
< 0.1%
324.43 2
< 0.1%
324.26 2
< 0.1%
323.59 2
< 0.1%
323.47 2
< 0.1%
323.45 2
< 0.1%
323.33 2
< 0.1%
322.4 2
< 0.1%
322.17 2
< 0.1%

Interactions

2024-07-09T07:55:23.489405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:49.768324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:53.338857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:56.890157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:00.224068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:03.448476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:06.816157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:10.147167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:13.547013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:16.903193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:20.115258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:23.767051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:50.071305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:53.661460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:57.210400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:00.494331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:03.724325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:07.080351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:10.431144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:13.853447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:17.182826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:20.384366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:24.056092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:50.462921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:53.959182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:57.506941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:00.781715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:04.015961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:07.360725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:10.731375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:14.149582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:17.483160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:20.653510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:24.428567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:50.800922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:54.288947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:57.824088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:01.100920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:04.324535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:07.644248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:11.077123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:14.446775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:17.783231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:20.935884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:24.724649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:51.161104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:54.556123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:58.098918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:01.375134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:04.592951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:07.901580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:11.384038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:14.736943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:18.067419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:21.201016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:25.055185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:51.521354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:54.864837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:58.382669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:01.706890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:04.866369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:08.183740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:11.685371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:15.047036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:18.346524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:21.485505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:25.418481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:51.786397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:55.188855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:58.645692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:01.965492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:05.132458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:08.450068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:11.981086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:15.347043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:18.621544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:21.751860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:25.734385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:52.105742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:55.708766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:58.957487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:02.262603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:05.499372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:08.759568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:12.302373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:15.663850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:18.922031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:22.071883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:26.048592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:52.425213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:56.013507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:59.341019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:02.598373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:05.825174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:09.046560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:12.635451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:15.973514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:19.240400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:22.583427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:26.350693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:52.723756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:56.312863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:59.647105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:02.894567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:06.175973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:09.565687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:12.978164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:16.279285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:19.548727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:22.900022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:26.634953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:52.988451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:56.583213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:54:59.912872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:03.159075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:06.481138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:09.850745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:13.253340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:16.584135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:19.815933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-09T07:55:23.155434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-07-09T07:55:42.955042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
CustomerIDChurnTenurePreferredLoginDeviceCityTierWarehouseToHomePreferredPaymentModeGenderHourSpendOnAppNumberOfDeviceRegisteredPreferredOrderCatSatisfactionScoreMaritalStatusNumberOfAddressComplainOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmount
CustomerID1.0000.0000.0600.0000.0000.0000.0000.0000.5290.5440.0000.5430.5500.0890.0000.1870.4210.1510.0590.441
Churn0.0001.0000.4810.0770.0500.1280.0750.0400.0400.1620.1840.0880.1100.0790.3820.0860.0260.0760.1830.247
Tenure0.0600.4811.0000.0720.0840.1050.0680.0730.0830.0790.4270.0600.1350.2610.0700.1100.1040.1930.1780.383
PreferredLoginDevice0.0000.0770.0721.0000.0000.0600.0300.0120.0370.0190.0450.0370.0150.0510.0000.0300.0000.0610.0340.053
CityTier0.0000.0500.0840.0001.0000.0030.5010.0270.0000.0240.2760.0760.1390.0410.0000.0880.0350.0530.0780.256
WarehouseToHome0.0000.1280.1050.0600.0031.0000.0350.0160.0090.0310.0770.0000.0340.0220.0710.0870.0210.0200.0000.065
PreferredPaymentMode0.0000.0750.0680.0300.5010.0351.0000.0350.0000.0000.2550.1200.0330.0750.0090.0460.0000.0830.0600.189
Gender0.0000.0400.0730.0120.0270.0160.0351.0000.0130.0100.0570.0270.0200.0260.0590.0480.0590.0640.0420.082
HourSpendOnApp0.5290.0400.0830.0370.0000.0090.0000.0131.0000.5330.0570.0000.0550.0690.0000.1400.2500.0700.0360.234
NumberOfDeviceRegistered0.5440.1620.0790.0190.0240.0310.0000.0100.5331.0000.0320.0000.0850.0590.0000.1620.2560.1100.1070.274
PreferredOrderCat0.0000.1840.4270.0450.2760.0770.2550.0570.0570.0321.0000.0460.0920.1620.0080.0760.3330.4580.3120.953
SatisfactionScore0.5430.0880.0600.0370.0760.0000.1200.0270.0000.0000.0461.0000.3000.0580.0350.0820.0320.0580.0450.031
MaritalStatus0.5500.1100.1350.0150.1390.0340.0330.0200.0550.0850.0920.3001.0000.0780.0000.0000.0470.0640.0750.111
NumberOfAddress0.0890.0790.2610.0510.0410.0220.0750.0260.0690.0590.1620.0580.0781.0000.0360.0830.0670.0990.1040.182
Complain0.0000.3820.0700.0000.0000.0710.0090.0590.0000.0000.0080.0350.0000.0361.0000.0520.0000.0000.0440.058
OrderAmountHikeFromlastYear0.1870.0860.1100.0300.0880.0870.0460.0480.1400.1620.0760.0820.0000.0830.0521.0000.1500.2070.0510.125
CouponUsed0.4210.0260.1040.0000.0350.0210.0000.0590.2500.2560.3330.0320.0470.0670.0000.1501.0000.7780.3510.365
OrderCount0.1510.0760.1930.0610.0530.0200.0830.0640.0700.1100.4580.0580.0640.0990.0000.2070.7781.0000.4780.434
DaySinceLastOrder0.0590.1830.1780.0340.0780.0000.0600.0420.0360.1070.3120.0450.0750.1040.0440.0510.3510.4781.0000.328
CashbackAmount0.4410.2470.3830.0530.2560.0650.1890.0820.2340.2740.9530.0310.1110.1820.0580.1250.3650.4340.3281.000
2024-07-09T07:55:43.558071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ChurnCityTierComplainGenderMaritalStatusPreferredLoginDevicePreferredOrderCatPreferredPaymentModeSatisfactionScore
Churn1.0000.0830.2490.0260.1820.0490.2250.0920.108
CityTier0.0831.0000.0000.0460.0420.0000.2160.4360.057
Complain0.2490.0001.0000.0380.0000.0000.0100.0120.042
Gender0.0260.0460.0381.0000.0330.0080.0690.0430.033
MaritalStatus0.1820.0420.0000.0331.0000.0240.0690.0250.238
PreferredLoginDevice0.0490.0000.0000.0080.0241.0000.0560.0370.045
PreferredOrderCat0.2250.2160.0100.0690.0690.0561.0000.0970.017
PreferredPaymentMode0.0920.4360.0120.0430.0250.0370.0971.0000.045
SatisfactionScore0.1080.0570.0420.0330.2380.0450.0170.0451.000

Missing values

2024-07-09T07:55:27.095294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-09T07:55:27.972364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-09T07:55:28.641131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CustomerIDChurnTenurePreferredLoginDeviceCityTierWarehouseToHomePreferredPaymentModeGenderHourSpendOnAppNumberOfDeviceRegisteredPreferredOrderCatSatisfactionScoreMaritalStatusNumberOfAddressComplainOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmount
05000114.0Phone36.0Debit CardFemale3.03Laptop & Accessory2Single9111.01.01.05.0159.93
1500021NaNPhone18.0UPIMale3.04Phone3Single7115.00.01.00.0120.90
2500031NaNPhone130.0Debit CardMale2.04Phone3Single6114.00.01.03.0120.28
35000410.0Phone315.0Debit CardMale2.04Laptop & Accessory5Single8023.00.01.03.0134.07
45000510.0Phone112.0Credit CardMaleNaN3Phone5Single3011.01.01.03.0129.60
55000610.0Computer122.0Debit CardFemale3.05Phone5Single2122.04.06.07.0139.19
6500071NaNPhone311.0Cash on DeliveryMale2.03Laptop & Accessory2Divorced4014.00.01.00.0120.86
7500081NaNPhone16.0Credit CardMale3.03Phone2Divorced3116.02.02.00.0122.93
850009113.0Phone39.0E walletMaleNaN4Phone3Divorced2114.00.01.02.0126.83
9500101NaNPhone131.0Debit CardMale2.05Phone3Single2012.01.01.01.0122.93
CustomerIDChurnTenurePreferredLoginDeviceCityTierWarehouseToHomePreferredPaymentModeGenderHourSpendOnAppNumberOfDeviceRegisteredPreferredOrderCatSatisfactionScoreMaritalStatusNumberOfAddressComplainOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmount
56205562103.0Phone135.0Credit CardFemale4.05Phone5Single3015.01.02.05.0162.85
562155622114.0Phone335.0E walletMale3.05Fashion5Married6114.03.0NaN1.0233.54
562255623013.0Phone331.0E walletFemale3.05Grocery1Married2012.04.0NaN7.0245.31
56235562405.0Computer112.0Credit CardMale4.04Laptop & Accessory5Single2020.02.02.0NaN224.36
56245562501.0Phone312.0UPIFemale2.05Phone3Single2019.02.02.01.0154.66
562555626010.0Computer130.0Credit CardMale3.02Laptop & Accessory1Married6018.01.02.04.0150.71
562655627013.0Phone113.0Credit CardMale3.05Fashion5Married6016.01.02.0NaN224.91
56275562801.0Phone111.0Debit CardMale3.02Laptop & Accessory4Married3121.01.02.04.0186.42
562855629023.0Computer39.0Credit CardMale4.05Laptop & Accessory4Married4015.02.02.09.0178.90
56295563008.0Phone115.0Credit CardMale3.02Laptop & Accessory3Married4013.02.02.03.0169.04